Tunable Photocontrolled Motions Using Stored Strain Energy in Malleable Azobenzene Liquid Crystalline Polymer Actuators
Bibliographic record
Abstract
A new strategy for enhancing the photoinduced mechanical force is demonstrated using a reprocessable azobenzene-containing liquid crystalline network (LCN). The basic idea is to store mechanical strain energy in the polymer beforehand so that UV light can then be used to generate a mechanical force not only from the direct light to mechanical energy conversion upon the trans-cis photoisomerization of azobenzene mesogens but also from the light-triggered release of the prestored strain energy. It is shown that the two mechanisms can add up to result in unprecedented photoindued mechanical force. Together with the malleability of the polymer stemming from the use of dynamic covalent bonds for chain crosslinking, large-size polymer photoactuators in the form of wheels or spring-like "motors" can be constructed, and, by adjusting the amount of prestored strain energy in the polymer, a variety of robust, light-driven motions with tunable rolling or moving direction and speed can be achieved. The approach of prestoring a controllable amount of strain energy to obtain a strong and tunable photoinduced mechanical force in azobenzene LCN can be further explored for applications of light-driven polymer actuators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".